---
license: cc-by-sa-4.0
task_categories:
  - text-generation
language:
  - en
tags:
  - math
  - calibration
  - rl
pretty_name: CONCORD training prompts
size_categories:
  - 1K<n<10K
---

# CONCORD training prompts

1600 math prompts used to train the CONCORD models from

> *CONCORD: Label-Free Calibration of Verbalized LLM Confidence via Rollout Consistency.*
> Maximilian Schall, Gerard de Melo, Padhraic Smyth. **COLM 2026.**

Sampled uniformly across difficulty levels from
[`OpenDataArena/ODA-Math-460k`](https://huggingface.co/datasets/OpenDataArena/ODA-Math-460k)
with seed 42. Only the prompt column (`question`) is retained — CONCORD is
label-free, so reference answers are intentionally not included.

- Source: `OpenDataArena/ODA-Math-460k`
- Subset size: 1600
- Columns: `question`
- Sampling: uniform over `difficulty`, seed 42

If you need reference answers, difficulty labels, or the full dataset,
use the original source directly.

## Citation

```bibtex
@inproceedings{schall2026concord,
  title     = {{CONCORD}: Label-Free Calibration of Verbalized {LLM} Confidence via Rollout Consistency},
  author    = {Schall, Maximilian and de Melo, Gerard and Smyth, Padhraic},
  booktitle = {Conference on Language Modeling (COLM)},
  year      = {2026}
}
```